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  <div class="headertitle"><div class="title">KernelBudgetedSGDTutorial.cpp</div></div>
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<a href="_kernel_budgeted_s_g_d_tutorial_8cpp.html">Go to the documentation of this file.</a><div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#include &lt;<a class="code" href="_kernel_budgeted_s_g_d_trainer_8h.html">shark/Algorithms/Trainers/Budgeted/KernelBudgetedSGDTrainer.h</a>&gt;</span> <span class="comment">// the KernelBudgetedSGD trainer</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#include &lt;<a class="code" href="_merge_budget_maintenance_strategy_8h.html">shark/Algorithms/Trainers/Budgeted/MergeBudgetMaintenanceStrategy.h</a>&gt;</span> <span class="comment">// the strategy the trainer will use </span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &lt;<a class="code" href="_data_distribution_8h.html">shark/Data/DataDistribution.h</a>&gt;</span> <span class="comment">//includes small toy distributions</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &lt;<a class="code" href="_gaussian_rbf_kernel_8h.html">shark/Models/Kernels/GaussianRbfKernel.h</a>&gt;</span> <span class="comment">//the used kernel for the SVM</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &lt;<a class="code" href="_hinge_loss_8h.html">shark/ObjectiveFunctions/Loss/HingeLoss.h</a>&gt;</span> <span class="comment">// the loss we want to use for the SGD machine</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span><span class="preprocessor">#include &lt;<a class="code" href="_zero_one_loss_8h.html">shark/ObjectiveFunctions/Loss/ZeroOneLoss.h</a>&gt;</span> <span class="comment">//used for evaluation of the classifier</span></div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span> </div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span><span class="keyword">using namespace </span><a class="code hl_namespace" href="namespaceshark.html" title="AbstractMultiObjectiveOptimizer.">shark</a>;</div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span><span class="keyword">using namespace </span>std;</div>
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno">   10</span> </div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span> </div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span><span class="comment">// data generating distribution for our toy</span></div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span><span class="comment">// multi-category classification problem</span></div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span><span class="keyword">class </span>myProblem : <span class="keyword">public</span> <a class="code hl_class" href="classshark_1_1_labeled_data_distribution.html" title="A LabeledDataDistribution defines a supervised learning problem.">LabeledDataDistribution</a>&lt;RealVector, unsigned int&gt;</div>
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno">   15</span>{</div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span><span class="keyword">public</span>:</div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classshark_1_1_labeled_data_distribution.html#ace4a2b81e54f0944d241b92a54f1a4c2" title="Generates a single pair of input and label.">draw</a>(RealVector&amp; input, <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span>&amp; label)<span class="keyword">const</span></div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span><span class="keyword">    </span>{</div>
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno">   19</span>        label = <a class="code hl_function" href="namespaceshark_1_1random.html#aa64d4174eaf7111b03e0504eaa56b666" title="Draws a discrete number in {low,low+1,...,high} by drawing random numbers from rng.">random::discrete</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>, 0, 4);</div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        input.resize(1);</div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>        input(0) = <a class="code hl_function" href="namespaceshark_1_1random.html#a972c5f7f031612a130aa077fc9136a9f" title="Draws a number from the normal distribution with given mean and variance by drawing random numbers fr...">random::gauss</a>(<a class="code hl_variable" href="namespaceshark_1_1random.html#ab5c1547eee483974d008d43f621a2234">random::globalRng</a>) + 3.0 * label;</div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span>    }</div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>};</div>
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno">   24</span> </div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span> </div>
<div class="foldopen" id="foldopen00026" data-start="{" data-end="}">
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno"><a class="line" href="_kernel_budgeted_s_g_d_tutorial_8cpp.html#a3c04138a5bfe5d72780bb7e82a18e627">   26</a></span><span class="keywordtype">int</span> <a class="code hl_function" href="_datasets_8cpp.html#ae66f6b31b5ad750f1fe042a706a4e3d4">main</a>(<span class="keywordtype">int</span> argc, <span class="keywordtype">char</span>** argv)</div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>{</div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span>    <span class="comment">// experiment settings</span></div>
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno">   29</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> ell = 500;     <span class="comment">// number of training data point</span></div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>    <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> tests = 10000; <span class="comment">// number of test data points</span></div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>    <span class="keywordtype">double</span> gamma = 0.5;         <span class="comment">// kernel bandwidth parameter</span></div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>    <span class="keywordtype">double</span> C = 1.0;          <span class="comment">// regularization parameter</span></div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span>    <span class="keywordtype">bool</span> bias = <span class="keyword">false</span>;           <span class="comment">// use bias/offset parameter</span></div>
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno">   34</span>    <span class="keywordtype">size_t</span> budgetSize = 16;     <span class="comment">// our model shall contain at most 16 vectors</span></div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>    <span class="keywordtype">size_t</span> epochs = 5;      <span class="comment">// we want to run 5 epochs</span></div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span> </div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span> </div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>    <a class="code hl_class" href="classshark_1_1_gaussian_rbf_kernel.html" title="Gaussian radial basis function kernel.">GaussianRbfKernel&lt;&gt;</a> kernel(gamma); <span class="comment">// Gaussian kernel</span></div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span>    <a class="code hl_struct" href="structshark_1_1_kernel_classifier.html" title="Linear classifier in a kernel feature space.">KernelClassifier&lt;RealVector&gt;</a> kernelClassifier; <span class="comment">// (affine) linear function in kernel-induced feature space</span></div>
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno">   40</span> </div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>    <span class="comment">// generate dataset</span></div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>    <a class="code hl_class" href="classshark_1_1_chessboard.html" title="&quot;chess board&quot; problem for binary classification">Chessboard</a> problem; <span class="comment">// artificial benchmark data</span></div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>    <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> trainingData = problem.<a class="code hl_function" href="classshark_1_1_labeled_data_distribution.html#ace15c1b51c87cd4b553427a55416b155" title="Generates a dataset with samples from from the distribution.">generateDataset</a>(ell);</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>    <a class="code hl_class" href="classshark_1_1_labeled_data.html">ClassificationDataset</a> testData = problem.<a class="code hl_function" href="classshark_1_1_labeled_data_distribution.html#ace15c1b51c87cd4b553427a55416b155" title="Generates a dataset with samples from from the distribution.">generateDataset</a>(tests);</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno">   46</span>    <span class="comment">// define the machine</span></div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>    <a class="code hl_class" href="classshark_1_1_hinge_loss.html" title="Hinge-loss for large margin classification.">HingeLoss</a> hingeLoss; <span class="comment">// define the loss we want to use while training</span></div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span>    <span class="comment">// as the budget maintenance strategy we choose the merge strategy</span></div>
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno">   49</span>    <a class="code hl_class" href="classshark_1_1_merge_budget_maintenance_strategy.html" title="Budget maintenance strategy that merges two vectors.">MergeBudgetMaintenanceStrategy&lt;RealVector&gt;</a> *strategy = <span class="keyword">new</span> <a class="code hl_class" href="classshark_1_1_merge_budget_maintenance_strategy.html" title="Budget maintenance strategy that merges two vectors.">MergeBudgetMaintenanceStrategy&lt;RealVector&gt;</a>();</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>    <a class="code hl_class" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html" title="Budgeted stochastic gradient descent training for kernel-based models.">KernelBudgetedSGDTrainer&lt;RealVector&gt;</a> kernelBudgetedSGDtrainer(&amp;kernel, &amp;hingeLoss, C, bias, <span class="keyword">false</span>, budgetSize, strategy);        <span class="comment">// create the trainer</span></div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span>    kernelBudgetedSGDtrainer.<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#ad5bd681e0b7e4f64f8ef3f031fe15396">setEpochs</a>(epochs);      <span class="comment">// set the epochs number</span></div>
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno">   52</span> </div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>    <span class="comment">// train the machine</span></div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;Training the &quot;</span> &lt;&lt; kernelBudgetedSGDtrainer.<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#a6479254636b6c638beec35a3ba495f5a" title="From INameable: return the class name.">name</a>() &lt;&lt; <span class="stringliteral">&quot; on the problem with a budget of &quot;</span> &lt;&lt; budgetSize &lt;&lt; <span class="stringliteral">&quot; and &quot;</span> &lt;&lt; epochs &lt;&lt; <span class="stringliteral">&quot; Epochs...&quot;</span> &lt;&lt; std::endl; <span class="comment">// Shark algorithms know their names</span></div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>    kernelBudgetedSGDtrainer.<a class="code hl_function" href="classshark_1_1_kernel_budgeted_s_g_d_trainer.html#afc6eff8c84cf39de20aaec579f1716b0">train</a>(kernelClassifier, trainingData);</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;RealVector&gt;</a> supportVectors = kernelClassifier.<a class="code hl_function" href="classshark_1_1_classifier.html#adf58b2ed9969bad9828772dd23c59c02" title="Return the decision function.">decisionFunction</a>().basis();  <span class="comment">// get a pointer to the support vectors of the model</span></div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span>    <span class="keywordtype">size_t</span> nSupportVectors = supportVectors.<a class="code hl_function" href="group__shark__globals.html#ga814e8b0028cc90dd2af69805e8f8a04d" title="Returns the total number of elements.">numberOfElements</a>();     <span class="comment">// get number of support vectors</span></div>
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno">   58</span>    std::cout &lt;&lt; <span class="stringliteral">&quot;We have &quot;</span> &lt;&lt; nSupportVectors &lt;&lt; <span class="stringliteral">&quot; support vectors in our model.\n&quot;</span>;   <span class="comment">// report</span></div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span> </div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>    <span class="comment">// evaluate</span></div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span>    <a class="code hl_class" href="classshark_1_1_zero_one_loss.html" title="0-1-loss for classification.">ZeroOneLoss&lt;unsigned int&gt;</a> loss; <span class="comment">// 0-1 loss</span></div>
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno">   62</span>    <a class="code hl_class" href="classshark_1_1_data.html" title="Data container.">Data&lt;unsigned int&gt;</a> output = kernelClassifier(trainingData.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>()); <span class="comment">// evaluate on training set</span></div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>    <span class="keywordtype">double</span> train_error = loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(trainingData.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), output);</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>    cout &lt;&lt; <span class="stringliteral">&quot;training error:\t&quot;</span> &lt;&lt;  train_error &lt;&lt; endl;</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>    output = kernelClassifier(testData.<a class="code hl_function" href="group__shark__globals.html#ga6f74e657c7e0c8a32b2456fb328bd653" title="Access to inputs as a separate container.">inputs</a>()); <span class="comment">// evaluate on test set</span></div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>    <span class="keywordtype">double</span> test_error = loss.<a class="code hl_function" href="classshark_1_1_zero_one_loss.html#acba6670d53701d50eed0ecdbc1114175" title="Return zero if labels == predictions and one otherwise.">eval</a>(testData.<a class="code hl_function" href="group__shark__globals.html#ga6328a5aa2570c01a5ac5f25076071663" title="Access to labels as a separate container.">labels</a>(), output);</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span>    cout &lt;&lt; <span class="stringliteral">&quot;test error:\t&quot;</span> &lt;&lt; test_error &lt;&lt; endl;</div>
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno">   68</span>}</div>
</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span> </div>
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